An early warning system against malicious activities for smart grid communications

Abstract

here is high expectation recently for machine-tomachine (M2M) communications over wired and wireless links. Various applications of M2M have already started to emerge in various sectors such as healthcare, vehicular ad hoc networks, smart home technologies, and so on [1]. The evolution of M2M has also begun in developing a smart power grid framework, referred to as the smart grid (SG) [2,3]. An electric grid having smart or intelligent capability allows power providers, distributors, and consumers -operating requirements and capabilities. Through this awareness, smart grid is able to produce, distribute, and consume power in the most effective manner. This type of communication takes place only among machines such as sensors, smart meters, and other equipment. Therefore, the M2M communications in SG must be private and secure since many of the autonomic functions that will run over it will be critical. Smart grid is usually portrayed as having numerous electrical appliances connected to one another in a complex manner so that they can report back on information such as power consumption and other monitoring signals. This promises higher efficiency in the power distribution networks (i.e., greater availability of power to homes and factories at lower cost), and will allow distributed power generation such as local solar and wind generators. It will reach into home-based devices; therefore, in addition to scalability and fast communication, serious attention should be paid to SG security [4]. Since SG communication is going to be based on current networking technologies, the same security concerns often encountered in conventional networks will also be prevalent in SG. In fact, cyber threats such as distributed denial of service (DDoS) attacks are likely to have more impact on SG communication because of the involvement of so much electrical equipment on the consumer side. By means of early forecasting of malicious threats to SG, it may be possible to take quick measures to protect the appliances from being compromised by the attacker. To establish such a forecasting framework, however, we need to consider the fact that humans are not supposed to interfere with M2M communication. Instead, the machines within the SG should have an adequate framework to predict or warn about malicious events, abnormalities, and failures a priori. The machines in the SG can be found in different types of networks, such as home, building, and neighborhood-wide networks. The smart meters deployed in these different networks may be utilized to form an information sharing network. Such information sharing can provide an important resource for developing global and timely assessments of emerging malicious threats against SG communication.While smart meters should authenticate other smart meters and devices, the authentication scheme itself can be targeted by DDoS attackers. In this article, we consider the spread of a worm in an SG that compromises a number of machines (i.e., smart meters, electrical appliances), which start sending malic...

A Survey of Emerging M2M Systems: Context, Task, and Objective

“…Generally, data analysis can help to make predictions based on collected data from many M2M devices during a specific period of time. A wide range of problems such as traffic congestion, power distribution anomalies (e.g., voltage level spikes), malicious attacks, and so on may fall into the scope of prediction [39]. The data mining technologies can be applied to analyze collected data from M2M devices.…”

“…Fadlullah et al [39] studied the energy M2M system for smart grid with a system-centric M2M task, i.e., fault prediction. Specifically, they modeled the malicious and/or abnormal events, which can compromise the security and privacy of smart grid users, as a Gaussian process.…”

“…Clearly, above solutions [37], [39], [53] are performanceoriented M2M systems. By contrast, the following solutions are for consumption-oriented M2M systems with static topology and device-centric tasks.…”

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“…Cyber security is of paramount importance in smart grid as communications are deeply involved in its operations [8], [9], [10], [11], [12], [13]. All the data transmitted in the grid must be authenticated and secured against malicious modification.…”